Systems and methods for performing simulations at a base station router

ABSTRACT

The present disclosure pertains to a base station (BS) apparatus configured to provide communication for one or more user equipment (UEs). Some embodiments of this apparatus may include: a BS networking device configured to access actual wireless data pertaining to actual UEs and to access simulated wireless data pertaining to simulated UEs; an interface controller communicably interposed between the BS networking device and the UEs, the interface controller being configured to control the access to the actual wireless data and the simulated wireless data; and a bridging device configured to convert between a wireless protocol used by the UEs and a wired protocol used by the BS networking device.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/587,267, filed Sep. 30, 2019, entitled “Systems and Methods forPerforming Simulations at a Base Station Router,” which is incorporatedby reference herein in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forpreparing and performing simulations for a networking system that can beenhanced with machine learning techniques. More particularly, thedisclosed approach relates to the development and testing of basestations, which support wireless communications, by simulating itswireless environment and by enabling machine learning of features of thecommunications system.

BACKGROUND

Development and complex, functional testing of base station networkingdevices (e.g., routers) for wireless (e.g., cellular) communicationssystems is exceedingly difficult, especially when considering thatdevelopers and testers lack ability to control or realistically simulateall aspects and constraints of an environment (e.g., in the wirelessspectrum) of the base station. There is thus a need for sufficientcontrol of a base station's wireless environment to effectively testone-off scenarios, such as when a new, legitimate base station or arogue, fraudulent base station is introduced into an existing network, abase station appears having an unknown neighbor list, and a mobiledevice appears having specific communication problems, etc.

Further, the cost of base station hardware limits their availability fordevelopment and testing, which exacerbates the need. And control of anactual, wireless environment is generally impractical because ofphysical limitations, government or legal constraints, and prohibitivecosts associated with such activities. As a result, problems are oftenleft unresolved until production systems go live, which may causeunacceptable consequences.

The development of machine learning algorithms, for use in controllingor enhancing wireless systems, may also be complicated for the samereasons. That is, without the ability to provide an environment with aknown, controlled state, it is difficult to capture suitable data andtrain a learning system. The lack of a controlled environment or arealistic, simulated environment prevents an ideal solution.

SUMMARY

Systems and methods are disclosed for providing a base station (BS)apparatus configured to facilitate communication for one or more userequipment (UEs). Accordingly, one or more aspects of this apparatus mayinclude: a base station networking device configured to access actualwireless data pertaining to actual UEs and to access simulated wirelessdata pertaining to simulated UEs; an interface controller communicablyinterposed between the base station networking device and the UEs, theinterface controller being configured to control the access to theactual wireless data and the simulated wireless data; and a bridgingdevice configured to convert between a wireless protocol used by the UEsand a wired protocol used by the BS networking device.

The method is implemented by a system comprising one or more hardwareprocessors configured by machine-readable instructions and/or othercomponents. The system comprises the one or more processors and othercomponents or media, e.g., upon which machine-readable instructions maybe executed. Implementations of any of the described techniques mayinclude a method or process, an apparatus, a device, a machine, asystem, or instructions stored on computer-readable storage device(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The details of particular implementations are set forth in theaccompanying drawings and description below. Like reference numerals mayrefer to like elements throughout the specification. Other features willbe apparent from the following description, including the drawings andclaims. The drawings, though, are for the purposes of illustration anddescription only and are not intended as a definition of the limits ofthe disclosure.

FIG. 1 illustrates an example of a system comprising base stations, userequipment, and a satellite that may be simulated by means of processingequipment and interface control switches, in accordance with one or moreembodiments.

FIG. 2 illustrates an example of a protocol stack that may be modifiedfor additionally supporting simulated traffic, in accordance with one ormore embodiments.

FIG. 3 illustrates a method for preparing a simulator for a networkingsystem that comprises base stations and user equipment, in accordancewith one or more embodiments.

FIG. 4 illustrates a method for preparing and using machine learningmodels for improving communication in the networking system, inaccordance with one or more embodiments.

FIG. 5 illustrates a method for preparing and using machine learningmodels for improving detection of fraudulent activity on a wired,backhaul network of the networking system, in accordance with one ormore embodiments.

DETAILED DESCRIPTION

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include,”“including,” and “includes” and the like mean including, but not limitedto. As used herein, the singular form of “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise. Asemployed herein, the term “number” shall mean one or an integer greaterthan one (i.e., a plurality).

As used herein, the statement that two or more parts or components are“coupled” shall mean that the parts are joined or operate togethereither directly or indirectly, i.e., through one or more intermediateparts or components, so long as a link occurs. As used herein, “directlycoupled” means that two elements are directly in contact with eachother.

Unless specifically stated otherwise, as apparent from the discussion,it is appreciated that throughout this specification discussionsutilizing terms such as “processing,” “computing,” “calculating,”“determining,” or the like refer to actions or processes of a specificapparatus, such as a special purpose computer or a similar specialpurpose electronic processing/computing device.

Presently disclosed are ways of implementing a controlled environmentfor development, testing, and training of base station networkingdevices. FIG. 1 illustrates system 10 configured to (i) transportwirelessly received data over wired means (e.g., while maintaining thesame formatting of the wireless data, such as cellular traffic, afterconversion at a physical layer), (ii) implement a base stationnetworking device that internally uses, e.g., an Ethernet (i.e., notradio frequency (RF)) interface for supporting wireless traffic, and/or(iii) support different combinations of real and virtual base stationsand of real and virtual user equipment (UEs). Some embodiments of system10 may provide a simulated environment with a known, controlled state,making it easier to capture suitable data and train a learning system.FIG. 1 thus further illustrates system 10 configured to prepare and useone or more prediction models that learn optimal operating parametersfor a base station networking device and that learn presence offraudulent devices (e.g., satellites, base stations, UEs, or anothernetworking device).

Electronic storage 22 of FIG. 1 comprises electronic storage media thatelectronically stores information. The electronic storage media ofelectronic storage 22 may comprise system storage that is providedintegrally (i.e., substantially non-removable) with system 10 and/orremovable storage that is removably connectable to system 10 via, forexample, a port (e.g., a USB port, a firewire port, etc.) or a drive(e.g., a disk drive, etc.). Electronic storage 22 may be (in whole or inpart) a separate component within system 10, or electronic storage 22may be provided (in whole or in part) integrally with one or more othercomponents of system 10 (e.g., a user interface device 18, processor 20,etc.). In some embodiments, electronic storage 22 may be located in aserver together with processor 20, in a server that is part of externalresources 24, in user interface devices 18, and/or in other locations.Electronic storage 22 may comprise a memory controller and one or moreof optically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage22 may store software algorithms, information obtained and/or determinedby processor 20, information received via user interface devices 18and/or other external computing systems, information received fromexternal resources 24, and/or other information that enables system 10to function as described herein.

External resources 24 may include sources of information (e.g.,databases, websites, etc.), external entities participating with system10, one or more servers outside of system 10, a network, electronicstorage, equipment related to Wi-Fi technology, equipment related toBluetooth® technology, data entry devices, a power supply, atransmit/receive element (e.g., an antenna configured to transmit and/orreceive wireless signals), a network interface controller (NIC), adisplay controller, a graphics processing unit (GPU), a radio (e.g., asoftware-defined radio), and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 24 may be provided by other components or resourcesincluded in system 10. Processor 20, external resources 24, userinterface device 18, electronic storage 22, network 70, base stations60, and/or other components of system 10 may be configured tocommunicate with each other via wired and/or wireless connections, suchas a network (e.g., a local area network (LAN), the Internet, a widearea network (WAN), a radio access network (RAN), a public switchedtelephone network (PSTN)), cellular technology (e.g., GSM, UMTS, LTE,5G, etc.), Wi-Fi technology, another wireless communications link (e.g.,radio frequency (RF), microwave, infrared (IR), ultraviolet (UV),visible light, cmWave, mmWave, etc.)), a base station, and/or otherresources.

User interface device(s) 18 of system 10 may be configured to provide aninterface between one or more users and system 10. User interfacedevices 18 are configured to provide information to and/or receiveinformation from the one or more users. User interface devices 18include a user interface and/or other components. The user interface maybe and/or include a graphical user interface configured to present viewsand/or fields configured to receive entry and/or selection with respectto particular functionality of system 10, and/or provide and/or receiveother information. In some embodiments, the user interface of userinterface devices 18 may include a plurality of separate interfacesassociated with processors 20 and/or other components of system 10.Examples of interface devices suitable for inclusion in user interfacedevice 18 include a touch screen, a keypad, touch sensitive and/orphysical buttons, switches, a keyboard, knobs, levers, a display,speakers, a microphone, an indicator light, an audible alarm, a printer,and/or other interface devices. The present disclosure also contemplatesthat user interface devices 18 include a removable storage interface. Inthis example, information may be loaded into user interface devices 18from removable storage (e.g., a smart card, a flash drive, a removabledisk) that enables users to customize the implementation of userinterface devices 18.

In some embodiments, user interface devices 18 are configured to providea user interface, processing capabilities, databases, and/or electronicstorage to system 10. As such, user interface devices 18 may includeprocessors 20, electronic storage 22, external resources 24, and/orother components of system 10. In some embodiments, user interfacedevices 18 are connected to a network (e.g., the Internet). In someembodiments, user interface devices 18 do not include processor 20,electronic storage 22, external resources 24, and/or other components ofsystem 10, but instead communicate with these components via dedicatedlines, a bus, a switch, network, or other communication means. Thecommunication may be wireless or wired. In some embodiments, userinterface devices 18 are laptops, desktop computers, smartphones, tabletcomputers, and/or other user interface devices.

Data and content may be exchanged between the various components of thesystem 10 through a communication interface and communication pathsusing any one of a number of communications protocols. In one example,data may be exchanged employing a protocol used for communicating dataacross a packet-switched internetwork using, for example, the InternetProtocol Suite, also referred to as TCP/IP. The data and content may bedelivered using datagrams (or packets) from the source host to thedestination host solely based on their addresses. For this purpose theInternet Protocol (IP) defines addressing methods and structures fordatagram encapsulation. Of course other protocols also may be used.Examples of an Internet protocol include Internet Protocol Version 4(IPv4) and Internet Protocol Version 6 (IPv6).

In some embodiments, processor 20 may belong to a user device, aconsumer electronics device, a mobile phone, a smartphone, a personaldata assistant, a digital tablet/pad computer, a wearable device, apersonal computer, a laptop computer, a notebook computer, a workstation, a server, a high performance computer (HPC), a vehiclecomputer, a game or entertainment system, a set-top-box or any otherdevice. As such, processor 20 is configured to provide informationprocessing capabilities in system 10. Processor 20 may comprise one ormore of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor 20 is shown in FIG. 1 as asingle entity, this is for illustrative purposes only. In someembodiments, processor 20 may comprise a plurality of processing units.These processing units may be physically located within the same device(e.g., a server), or processor 20 may represent processing functionalityof a plurality of devices operating in coordination (e.g., one or moreservers, user interface devices 18, devices that are part of externalresources 24, electronic storage 22, and/or other devices).

As shown in FIG. 1, processor 20 is configured via machine-readableinstructions to execute one or more computer program components. Thecomputer program components may comprise one or more of interfacecontrol component 30, training component 32, UE simulation component 34,global positioning system (GPS) simulation component 36, base station(BS) simulation component 38, and/or other components. Processor 20 maybe configured to execute components 30, 32, 34, 36, and/or 38 by:software; hardware; firmware; some combination of software, hardware,and/or firmware; and/or other mechanisms for configuring processingcapabilities on processor 20.

It should be appreciated that although components 30, 32, 34, 36, and 38are illustrated in FIG. 1 as being co-located within a single processingunit, in embodiments in which processor 20 comprises multiple processingunits, one or more of components 30, 32, 34, 36, and/or 38 may belocated remotely from the other components. For example, in someembodiments, each of processor components 30, 32, 34, 36, and 38 maycomprise a separate and distinct set of processors. The description ofthe functionality provided by the different components 30, 32, 34, 36,and/or 38 described below is for illustrative purposes, and is notintended to be limiting, as any of components 30, 32, 34, 36, and/or 38may provide more or less functionality than is described. For example,one or more of components 30, 32, 34, 36, and/or 38 may be eliminated,and some or all of its functionality may be provided by other components30, 32, 34, 36, and/or 38. As another example, processor 20 may beconfigured to execute one or more additional components that may performsome or all of the functionality attributed below to one of components30, 32, 34, 36, and/or 38.

In some embodiments, a base station networking device may be a basestation router (BSR) or another networking device of a central node.This base station equipment may conduct mobile telephony, wirelesscomputer networking, and/or other wireless communications. In someembodiments, base station apparatus 60 may comprise one or more of arelay, station, repeater, hub, switch, repeater, bridge, router, and/oranother networking device. In some embodiments, base station apparatus60-1 may be configured to route traffic to and from UEs 90, base stationapparatus 60-z, processor 20, and satellite 80.

In some embodiments, base station apparatus 60 may be comprised of abase station networking device and a set of other components. Thesecomponents may include processors, busses, communication interfaces,interface enabling components (e.g., for multi-carrier communications),databases, memory devices, protocol converters, a power supply,antennas, and a tower, pole, or another suitable structure havingsufficient dimension(s). The base station networking device forms partof known base station equipment, such as an E-UTRAN Node B or an evolvednode (eNodeB). Each base station apparatus may comprise a plurality ofantennas (e.g., at least 1 for cellular and at least 1 for the GPS).

In some embodiments, the base station networking device of base stationapparatus 60 may comprise a plurality of different processors, such as aLinux control processor, common cellular processor, call controlprocessor, and/or other suitable processor(s). The CCP may perform callcontrol functionality (e.g., setup and teardown requests of a call),paging functionality, functionality of selector assignment and teardown,etc. according to known, mobile communication networks. The CCP may alsomanage information generated during call processing, e.g., includingcall status information of each selector, traffic channel address,selector address, assignment information, cell ID (identifier)information, and the like.

In some embodiments, base station apparatus 60 may comprise a userinterface (UI) for the base station networking device. Via this UI, auser may control at least some features of base station apparatus 60.

In some embodiments, user equipment 90-91 may be a user device, aconsumer electronics device, a mobile phone, a smartphone, a personaldata assistant, a digital tablet/pad computer, a wearable device, apersonal computer, a laptop computer, a notebook computer, a workstation, a server, a high performance computer (HPC), a vehiclecomputer, a game or entertainment system, a set-top-box or any otherdevice. In some embodiments, UEs 90-91 communicate wirelessly (e.g., viaone or more antennas, base stations, repeaters, cellular over IP viaWi-Fi, etc.). In some embodiments, user equipment 90-91 may communicatewith one or more of each other via service performed by base stations60.

In some embodiments, base station 60-1 may communicate with userequipment 90-1 to 90-x (x being any set of natural numbers). Similarly,some embodiments of system 10 may comprise base station(s) 60-z (z beingany set of natural numbers) communicating with user equipment 91-1 to91-y (y being any set of natural numbers). These communications may beperformed via a wireless connection of any suitable protocol. Userequipment 91 may be in a different cell or region (e.g., of a respectivebase station) from user equipment 90.

In some embodiments, processor(s) 20 communicates locally with basestation(s) 60 (e.g., LAN, bus, or another suitable networking protocol).In other embodiments, processor(s) 20 communicates remotely with basestation(s) 60 (e.g., wired or wireless channel or network). UEs 90-91may also communicate remotely from base station apparatus 60. As such, adata source may be local to or remote from the base station.

In some embodiments, base station apparatuses 60 may communicate withsatellite(s) 80 via, e.g., GPS or another suitable satellitecommunications protocol in one or more RF and/or microwave bands. Forexample, base station apparatuses 60 may obtain forward link data fromsatellite(s) 80 and/or transmit return link data to satellite(s) 80.

In some embodiments, bridge component(s) 50 may convert actual wirelessdata received via one or more antennas from UEs 90-91 into packets orother protocol data units (PDUs), which are communicable to the basestation networking device(s) via one or more wired (e.g., electrical,optical, etc.) means. Similarly, some embodiments of bridge component(s)51 may convert actual wireless data received via one or more antennasfrom satellite 80 into PDUs, which are communicable to the base stationnetworking device(s) via the one or more wired means. As such, bridges50-51 may be coupled to respective inputs, i.e., from differentantennas. Bridges 50-51 may each operate as a computer networking bridgethat creates a single aggregate network from multiple communicationnetworks or network segments.

In some embodiments, interface controller(s) 55-56 may make (e.g.,restore, connect, etc.) or break (e.g., interrupt, disconnect, etc.)connections for network traffic. For example, interface controller 55may switch PDUs between UE simulation component 34 (which may emulateuser equipment as virtual UEs), user equipment 90, and the base stationnetworking device in or more directions of PDU traffic flow. In anotherexample, interface controller 56 may switch traffic between GPSsimulation component 36 (which may emulate GPS devices as virtualsatellites), satellite(s) 80, and the base station network device.

In some embodiments, interface controller 55 may comprise a hub, switch,repeater, bridge, or router. Similarly, some embodiments of interfacecontroller 56 may comprise a hub, switch, repeater, bridge, or router.In some embodiments, switches 55-56 may be directly coupled at oneinterface to a base station network device, and they may be directlycoupled at another interface to bridges 50-51, respectively.

In some embodiments, interface control component 30 may controlinterface controllers 55-56 in switching or relaying traffic to and/orfrom the base station networking device. For example, interface controlcomponent 30 may instruct interface controller 55 to: only receiveand/or transmit traffic between user equipment 90 and the base stationnetworking device; only receive and/or transmit traffic between UEsimulation component 34 and the base station networking device; and/orreceive and/or transmit traffic between user equipment 90, UE simulationcomponent 34, and the base station networking device. In anotherexample, interface control component 30 may instruct interfacecontroller 56 to: only receive and/or transmit traffic between satellite80 and the base station networking device; only receive and/or transmittraffic between GPS simulation component 36 and the base stationnetworking device; and/or receive and/or transmit traffic betweensatellite 80, GPS simulation component 36, and the base stationnetworking device.

In some embodiments, an interface of bridge 50 may implement one or morewireless protocols, such as 2G cellular, 3G cellular, 4G cellular, 4Glong term evolution (LTE) cellular, 5G cellular, Wi-Fi, radio frequencyidentification (RFID), Bluetooth, and/or Zigbee. In some embodiments,another interface of bridge 50 may implement one or more wiredprotocols, such as Ethernet, universal serial bus (USB), synchronousoptical networking (SONET), synchronous digital hierarchy (SDH),point-to-point protocol (PPP), high-level data link control (HDLC),digital subscriber line (DSL), integrated services digital network(ISDN), fiber distributed data interface (FDDI), and advanced datacommunication control procedures (ADCCP).

In some embodiments, at least some components of processor(s) 20 areintegrated into base station apparatus 60. In these or otherembodiments, at least some components of processor(s) 20 are distinctand separate from base station apparatus 60. As such, one or more ofthese software components may be used standalone or be integrated intoan existing cellular system.

Some embodiments of system 10 include modifying existing hardware andsoftware of a base station to support both an actual, physicalenvironment and a virtual, simulated environment. A higher degree ofcontrollability may be associated with the latter environment, which mayrepresent a portion of the actual, physical environment and/or ofanother environment. In some embodiments, components 34, 36, and/or 38of processors 20 may cause simulations that provide absolute controlover a (e.g., closed) environment. A closed network is defined herein asany type of network that is not available to outside devices or networksand thus that does not include an actual, wireless environment. Theclosed network may be as simple as a single base station networkingdevice and one piece of user equipment that communicates via Ethernet(wired) rather than cellular (wireless). But this closed network may beconnected to another, actual base station networking device of anotherbase station apparatus that is interfacing with the actual, wirelessenvironment. The closed network may be controlled for performingsimulations (e.g., a stand-alone simulation).

In some embodiments, processor(s) 20 may communicate with base station60-1 and/or 60-z (z being any set of natural numbers). And base station60-1 may communicate with at least some of base station 60-z and/or withnetwork 75, network 75 exemplarily being the Internet or another WAN.These backhaul communications exemplarily depicted in FIG. 1 may beperformed via a wired (e.g., Ethernet, coaxial, twisted-pair, bus, fiberoptic, and/or another suitable network connection), wireless (e.g.,microwave frequencies, RF, microwave, infrared (IR), ultraviolet (UV),visible light, cmWave, mmWave), mesh-topology, and/or edge-topologynetwork or link. In some embodiments, processor 20's backhaul access maybe facilitated by external resources 24.

In some embodiments, UE simulation component 34 may perform simulationof one or more user equipment. For example, UE simulation component 34may generate and/or obtain traffic for testing the impact ofinterference on the base station networking device of an existingnetwork. With respect to this generating, UE simulation component 34 maycreate simulated wireless data and transmit the simulated data byimplementing virtual UEs. Alternatively or additionally, UE simulationcomponent 34 may replay recorded, actual data. This actual, wirelessdata may comprise live data from existing or newly-installed cellularsystem(s). As such, the disclosed approach may exemplarily be performedby modifying an existing base station by inserting within it (or bycoupling to it) bridges 50-51 and switches 55-56 and by supportinginterface to processor(s) 20. The base station networking device maycontinue to process data as normal, without any consideration of thedata source and without modification to its base code. This ensures thatall testing and development is on actual hardware and software of a basestation networking device, which improves the result and quality offinal products.

In some embodiments, UE simulation component 34 may implement acontrolled environment. For example, UE simulation component 34 maysimulate multipath effects (e.g., in any RF band), different levels ofcongestion, many different user equipment including UEs associated withother networks or providers, interference from a variety of manmadesources like machinery and buildings, environmental sources likeweather, canyons and effects of other topography, and even solar flares.UE simulation component 34 may thus simulate RF interference, e.g., bysending garbage data via a wired Ethernet interface to test whether thebase station networking device is robust enough to maintain properoperation of other communication and to handle problems gracefully.

As it is impractical to control the physical environment of basestations and user equipment, some embodiments of UE simulation component34 may generate simulated RF data, which is obtained as or converted to(e.g., Ethernet) packets, the conversion being exemplarily performed bybridge 50 of base station apparatus 60. Bridge 50 may exemplarilyconvert wireless signals to such packet format. As such, byincorporating switch 55 into base station apparatus 60, cellular signalsmay be received and transmitted over a wired protocol (e.g., Ethernet)without disrupting design of the base station network device. Thisincorporation may enable automated, repeatable scenario testing anddevelopment, and it may enhance the training of machine learningalgorithms for wireless (e.g., cellular) systems.

In some embodiments, UE simulation component 34 may propagate actualcellular data as Ethernet packets to simulate the RF energy received byan antenna for testing of a base station networking device. For example,UE simulation component 34 may generate that data for transmission toswitch 55, which may forward the data to the base station networkingdevice under control of interface control component 30. Switch 55 may befurther configured to process data to and/or from actual UEs 90.

In some embodiments, interface control component 30 may control switch55 to switch between a physical source of cellular data and simulatedcellular data, making it possible to provide a realistic, controlledenvironment for base station apparatus 60. For example, switch 55 mayobtain Ethernet packets from a conversion output of bridge 50, forinjecting these packets (which simulate wireless traffic) into a stacklayer directly at the base station networking device. In this example,when obtaining Ethernet packets, the real cellular data may be stoppedor blocked by automated control via interface control component 30and/or by user control. Simulated, cellular data may exemplarily feedthis existing RF chain (i.e., a stack of networking layers, at least aportion of which is depicted in FIG. 2) with data that base stationapparatus 60 may expect. FIG. 2 thus exemplarily depicts injection(e.g., via a multiplexer that can select between converted cellular dataand the payload of simulated wireless data, this selection potentiallygiving priority to the actual or simulated data) of Ethernet traffic atone, upper level of the LTE stack of the base station networking device.For the reverse direction, this stack may comprise an extractor thatpulls data from the base station networking device and places this datainto the payload of newly constructed Ethernet packets, for transmittingfrom base station apparatus 60.

In some embodiments, switch 55 may enable or disable real, cellulardata. Similarly, in some embodiments, switch 56 may enable or disablereal, GPS data. When enabled, these switches may cause the base stationnetworking device to support real and simulated data simultaneously,providing more effective testing than can otherwise be performed witheither form of this data. In some embodiments, switch 55 may selectivelycontrol the base station networking device's access to actual andsimulated wireless data. As such, switch 55 may be communicablyinterposed between the base station networking device and the sources ofactual and simulated RF data. Similarly, switch 56 may be communicablyinterposed between the base station networking device and the sources ofactual and simulated GPS data.

Effectively, the base station networking device may not differentiatebetween actual data received from an antenna and simulated data receivedvia a wired protocol. Complex scenarios that are impractical toreproduce in the physical world may then be simulated by system 10, foruse in development of product solutions. That is, system 10 may not onlyfacilitate standard testing but also one-off testing, e.g., with uniqueand custom constraints. These unique constraints may test performance ofany setting or parameter of a base station networking device. Forexample, UE simulation component 34 may cause simulation of buildinginterference for the base station networking device. In another example,UE simulation component 34 may cause simulation of a very large numberof UEs (e.g., at a sports event).

In some embodiments, the base station networking device of base stationapparatus 60 may switch signals via at least switch 55, which may becontrolled by interface control component 30. For example, thisswitching may allow cellular data signals to come from actual UEs 90and/or simulated UEs, which are simulated by UE simulation component 34.In some embodiments, interface control component 30 may control switches55-56 and optionally even a switch for controlling traffic with respectto actual and simulated base stations. Interface control component 30may comprise an application programming interface (API) for interactionwith an overall simulation or automation tools, e.g., for supportingcommands.

In some embodiments, UE simulation component 34 may obtain (e.g., beforereaching the base station networking device) and store direct copies orrecords of the actual wireless (e.g., cellular RF) data into electronicstorage 22. The actual data may be then used exemplarily as simulatedwireless data in a simulation for the base station networking device. Insome embodiments, UE simulation component 34 may capture actual traffic(e.g., from actual UEs 90) and then replay that traffic in a controlledway (e.g., via simulated UEs). In some embodiments, UE simulationcomponent 34 may capture data that is strategically created to confirmthat the result is correct (e.g., via one or more comparisons) and thusthat the base station networking device is properly functioning. In someembodiments, UE simulation component 34 may control the simulatedenvironment by restricting or causing interference (e.g., erroneousdata).

In some embodiments, interface control component 30 and UE simulationcomponent 34 may operate together to enable custom scenarios, injectionof events, scenario replay, and/or automation. The disclosed approachmay thus be automated, e.g., allowing for repetitive testing andverification with respect to intermittent problems. For example, asingle test may be run many times to identify problems that only occuroccasionally. And base station apparatus 60 may be tested for resolutionof an intermittent problem by automatically varying one or more of basestation networking device parameter settings, simulated wirelessinterference, and a number of the simulated UEs that are generatingtraffic to a level that satisfies a congestion criterion. In someembodiments, interface control component 30, training component 32, andUE simulation component 34 may operate together to enable predictionalgorithms.

In some embodiments, UE simulation component 34 may simulate presence ofone or more user equipment. For example, UE simulation component 34 maygenerate and/or receive traffic with same or similar characteristics astraffic to/from user equipment 90. In this example, the traffic mayresemble cellular data. In some embodiments, UE simulation component 34may obtain traffic recorded in electronic storage 22 for replaying orinjecting at the base station networking device of base stationapparatus 60. For example, UE simulation component 34 may facilitatecomplex, functional testing of the base station networking device underspecific constraints. As such, data converted by bridge 50 or 51 may bestored for future playback at this local base station networking deviceor at the base station networking device of another base stationapparatus.

In some embodiments, UE simulation component 34 may analyze reception oftraffic from the base station networking device. In some embodiments, UEsimulation component 34 may determine presence of one or more anomaliesin actual traffic. UE simulation component 34 may then store thistraffic at electronic storage 22, for subsequent testing of the basestation networking device's ability to support this same traffic (e.g.,after a repair is attempted). For example, the base station networkingdevice may be modified in one or more different ways to producealternative outcomes, with the same, actual test patterns beingrepeatably injected.

In some embodiments, GPS simulation component 36 simulates GPS data(e.g., by creating it with an algorithm or heuristic, by capturingactual GPS data then replaying, or by another means). For example, GPSsimulation component 36 may prevent base station apparatus 60 fromactually having to be in a particular location. This prevention, bycausing emulation of base station apparatus 60 being in a differentlocation, may be beneficial, e.g., in lab environments.

In some embodiments, GPS simulation component 36 may simulate presenceof one or more satellites. For example, GPS simulation component 36 maygenerate and/or receive data with same or similar characteristics asdata to/from satellite 80. In this example, the data may resemble GPSdata. In some embodiments, GPS simulation component 36 may obtain datarecorded in electronic storage 22 for replaying or injecting at the basestation networking device of base station apparatus 60. In someembodiments, GPS simulation component 36 may analyze reception of datafrom the base station networking device.

An artificial neural network is a computational model that may, in someembodiments, be configured to determine and detect a base stationnetworking device's proper response to network traffic (e.g., RFtraffic, satellite traffic, and/or backhaul traffic). An artificialneural network is a network or circuit of artificial neurons or nodesfor solving artificial intelligence (AI) problems by operating aslearning algorithms that model the input-output relationship. Suchartificial networks may be used for predictive modeling. The predictionmodels may be and/or include one or more neural networks (e.g., deepneural networks, artificial neural networks, or other neural networks),other machine learning models, or other prediction models. As anexample, the neural networks referred to variously herein may be basedon a large collection of neural units (or artificial neurons). Neuralnetworks may loosely mimic the manner in which a biological brain works(e.g., via large clusters of biological neurons connected by axons).Each neural unit of a neural network may be connected with many otherneural units of the neural network. Such connections may be enforcing orinhibitory, in their effect on the activation state of connected neuralunits. These neural network systems may be self-learning and trained,rather than explicitly programmed, and can perform significantly betterin certain areas of problem solving, as compared to humans andtraditional computer programs. In some embodiments, neural networks mayinclude multiple layers (e.g., where a signal path traverses from inputlayers to output layers). In some embodiments, back propagationtechniques may be utilized to train the neural networks, where forwardstimulation is used to reset weights on the “front” neural units. Insome embodiments, stimulation and inhibition for neural networks may bemore free-flowing, with connections interacting in a more chaotic andcomplex fashion.

Disclosed implementations of artificial neural networks may apply aweight and transform the input data by applying a function, thistransformation being a neural layer. The function may be linear or, morepreferably, a nonlinear activation function, such as a logistic sigmoid,Tanh, or rectified linear activation function (ReLU) function.Intermediate outputs of one layer may be used as the input into a nextlayer. The neural network through repeated transformations learnsmultiple layers that may be combined into a final layer that makespredictions. This learning (i.e., training) may be performed by varyingweights or parameters to minimize the difference between the predictionsand expected values. In some embodiments, information may be fed forwardfrom one layer to the next. In these or other embodiments, the neuralnetwork may have memory or feedback loops that form, e.g., a neuralnetwork. Some embodiments may adjust parameters via back-propagation.

In some embodiments, a convolutional neural network (CNN) may be used. ACNN is a sequence of hidden layers, such as convolutional layersinterspersed with activation functions. Typical layers of a CNN are thusa convolutional layer, an activation layer, batch normalization, and apooling layer. Each output from one of these layers is an input for anext layer in the stack, the next layer being, e.g., another one of thesame layer or a different layer. For example, a CNN may have twosequential convolutional layers. In another example, a pooling layer(e.g., maximum pooling, average pooling, etc.) may follow aconvolutional layer. When many hidden, convolutional layers arecombined, this is called deep stacking.

Some embodiments may implement specific, machine-learning algorithms toa level acceptable for a particular application. For example, trainingcomponent 32 may identify anomalous behavior on a network accessible bythe base station networking device with a high level of accuracy. Inthis or other examples, training component 32 may employ amachine-learner that is well-trained to detect certain behavior. Forexample, model 40-2 may implement a machine learning (e.g., binary)classifier, including one or more of a naïve-Bayes classifier,self-organizing maps, clustering analysis, support vector machine (SVM),linear discriminant analysis, time frequency pattern analysis, singularvalue decomposition (SVD), artificial neural network, deep neuralnetwork (DNN), recurrent neural network (RNN), convolutional neuralnetwork (CNN), hidden Markov model (HMM), and Bayesian network (BN).This classifier may be an AI training model.

In some embodiments, models 40-2 may comprise machine learning modelsthat are developed using simulated data (e.g., from electronic storage22). The simulated data may thus help with the creation of the learningsystem because development needs known values to create and test amachine learning algorithm. With known, input values, developers ofmodels 40-2 may compare actual results with expected results to see ifthe algorithm is working properly. Once created, these models may befurther trained using actual wireless data (e.g., from UEs 90-91). Thatis, created models may be fed live data that acts as training data,i.e., when the training and learning of the model begins.

By capturing actual cellular traffic to collect training data and byplaying back cellular traffic to develop machine learning algorithms ata base station networking device in a simulated RF environment, deeplearning may be employed. For example, data statistics may be used tomodify environmental parameters for enhanced communications. In anotherexample, behavioral or pattern analysis may be applied to actualcellular data in a given area, e.g., to predict usage or capacitychanges for the base station networking device. In some embodiments,prediction database 40 (e.g., which may include prediction algorithms oranalysis of predictive behavior) may be utilized to determine a varietyof different things. For example, training component 32 in conjunctionwith prediction database 40 may identify when a major sporting event isgoing to require more user capacity on a wireless system and/ordetermine that a suburb is going to require more capacity in theevenings.

The above-described machine learning approach may be applied on anyinterface of base station apparatus 60. For example, these techniquesmay be performed in relation to wireless data of both actual andsimulated UEs. But this is not intended to be limiting, as thecontemplated approach includes developing and training one or moremachine learning models 40-2 that improve ability of the base stationnetworking device to support actual and simulated satellites. Similarly,the contemplated approach includes developing and training one or moremachine learning models 40-2 that improve ability of the base stationnetworking device to support actual and simulated BSs. Further, models40-2 may be developed and trained to detect fraudulent UEs, fraudulentsatellites, and/or fraudulent base stations (BSs). As such, trainedmodels 40-2 may be used to simulate a base station networking device'sability to properly process wireless data and to properly detectsignificant anomalies.

In some embodiments, training component 32 may generate and/or obtaintraining data 40-1. In some embodiments, training component 32 may,prior to training, divide or split training data 40-1 into a trainingdataset and a validation dataset, the subsequent training only beapplied to the training dataset. Training component 32 may then trainmachine-learning models 40-2 to learn relevant features of networktraffic. At least some of training data 40-1 may comprise actualtraffic, whether this actual traffic be wireless (e.g., cellular,satellite, etc.) or wired (e.g., Ethernet backhaul).

In some embodiments, training component 32 may prepare one or moreprediction models 40-2. In some embodiments, prediction model 40-2 maybe used to analyze predictions against a reference set of data calledthe validation set. In some use cases, the reference outputs may beprovided as input to the prediction models, which the prediction modelmay utilize to determine whether its predictions are accurate, todetermine the level of accuracy or completeness with respect to thevalidation set data, or to make other determinations. Suchdeterminations may be utilized by the prediction models to improve theaccuracy or completeness of their predictions. In another use case,accuracy or completeness indications with respect to the predictionmodels' predictions may be provided to the prediction model, which, inturn, may utilize the accuracy or completeness indications to improvethe accuracy or completeness of its predictions. For example, a labeledtraining set may enable model improvement. That is, the training modelmay use a training set of data to iterate over model parameters untilthe point where it arrives at a final set of parameters/weights to usein the model.

In some embodiments, training component 32 may perform modeling and/ormodel-tuning. Prediction models 40-2 (e.g., implementing a neuralnetwork) may be trained using training data 40-1 obtained by trainingcomponent 32 from prediction storage/database 40 (or from electronicstorage 22, external resources 24, and/or via user interface device(s)18). The validation set may be a subset of training data 40-1, which iskept hidden from the model to test accuracy of the model. The test setmay be a dataset of actual network traffic, which is new to the model totest accuracy of the model.

As mentioned, training component 32 may enable one or more predictionmodels to be trained. The training may be performed via severaliterations. For each training iteration, a classification prediction maybe determined and compared to the corresponding, known classification.For example, known simulated data may be input at system 10, during thetraining or validation, to determine whether the prediction model canproperly predict a response (e.g., determine a coverage parameter,determine a capacity parameter, or identify a fraudulent node). As such,the neural network is configured to receive at least a portion of thetraining data as an input feature space. Once trained, model(s) 40-2 maybe stored, as shown in FIG. 1, and then used subsequently with actual(e.g., live) network traffic.

In some embodiments, during training, training component 32 may use alabelled dataset to train both automatic feature extraction andclassification. Preprocessing may depend on the choice ofmachine-learning classifier. Backpropagation may then be applied to thetruth labels provided by the dataset, i.e., to train the network. Someembodiments may choose an optimizer on a per application basis.

In some embodiments, training component 32 may train on generic networkmetadata features, such as external IPs addressed, external portsaddressed, TCP flags observed, protocols observed, etc.

Some embodiments may multiply pass the training data forwards andbackwards. Once trained, the neural network may make predictions orinferences when fed input data. Some embodiments may implementartificial neural networks with one or more GPUs. And this is at leastbecause neural networks are created from large numbers of identicalneurons that are configured in parallel. This parallelism maps well toGPUs, which provide a significant computation speed-up over CPU-onlytraining. As such, embodiments that include use of GPU(s) may enablecloser to real-time capabilities of anomalous and/or fraudulentdetection.

As depicted in FIG. 2, switch 55 may be added to protocol stack layer 2,3, or another layer, in some implementations. In some embodiments, UEsimulation components 34 may transmit and/or receive data to and/or fromthe base station networking device at the non-access stratum (NAS) level(e.g., layer 3 of an interconnection model such as the open systemsinterconnection (OSI) model). In other embodiments, UE simulationcomponents 34 may transmit and/or receive data to and/or from the basestation networking device at the radio resource control (RRC) level(e.g., layer 3 of the model). In other embodiments, UE simulationcomponents 34 may transmit and/or receive data to and/or from the basestation networking device at the IP level (e.g., layer 3 of the model).FIG. 2 thus depicts an injection of data at a network layer of an LTEprotocol stack. But this is not intended to be limiting as any existingprotocol stack may be similarly modified for supporting injection ofsimulated data. Further, another protocol stack may be similarlymodified for supporting injection of simulated GPS data, and stillanother protocol stack may be similarly modified for supportinginjection of simulated backhaul data. In some embodiments, a singleprotocol stack of the base station networking device may supportwireless traffic of actual or simulated UEs, wireless traffic of actualor simulated satellites, and backhaul traffic of actual or simulatedbase stations.

In some embodiments, data is pulled from a suitable level or layer ofthe base station networking device. That is, rather than forwarding touser equipment 90-91, UE simulation components 34 may obtain this datato emulate reception of wireless data at a simulated user equipment. Inother embodiments, UE simulation components 34 may transmit and/orreceive data to and/or from the base station networking device using thepacket data convergence protocol (PDCP) (e.g., layer 2 of the model). Inother embodiments, UE simulation components 34 may transmit and/orreceive data to and/or from the base station networking device via radiolink control (RLC) (e.g., layer 2 of the model). In other embodiments,UE simulation components 34 may transmit and/or receive data to and/orfrom the base station networking device via medium access control (MAC)(e.g., layer 2 of the model). In other embodiments, UE simulationcomponents 34 may transmit and/or receive data to and/or from the basestation networking device at the physical layer (e.g., a lowest layer 1of the model).

As depicted in FIG. 2, NAS is a functional layer in the UMTS and LTEwireless telecom protocol stacks between the core network and UEs. Thislayer may be used to manage the establishment of communication sessionsand for maintaining continuous communications with the user equipment asit moves. NAS may be a protocol for messages passed between the UEs andcore nodes, e.g., transparently through the radio network. The NASprotocol may support the mobility of the UE and the session managementprocedures to establish and maintain IP connectivity.

The RRC protocol is used in UMTS and LTE on the air interface, as alayer that exists between UE and base station and that exists at the IPlevel. The RRC protocol may be used to establish connection and releasefunctions, broadcast system information, establish radio bearer,reconfigure and release, implement RRC connection mobility procedures,notify and release pages, and control outer loop power. The RRC mayfurther configure the user and control planes according to a networkstatus and allow for management strategies to be implemented.

RLC is a layer 2 protocol, being responsible for transfer of upper layerPDUs, error correction through ARQ, concatenation, segmentation, andreassembly. RLC may also be responsible for re-segmentation of RLC dataPDUs, reordering of RLC data PDUs, duplicating detection, discardingdata, re-establishing RLC, and detecting protocol error. RLC may operatein three modes: transparent mode (TM), unacknowledged mode (UM), andacknowledged mode (AM).

The MAC layer is another layer 2 protocol. More particularly, MAC is asublayer that controls the hardware responsible for interaction with thewired, optical, or wireless transmission medium. The MAC may provideflow control and multiplexing. The MAC layer may be responsible formapping between logical channels and transport channels, multiplexing ofMAC data from one or different logical channels onto transport blocks(TB) to be delivered to the physical layer on transport channels,de-multiplexing of data from one or different logical channels from TBsdelivered from the physical layer on transport channels, schedulinginformation reporting, error correction through HARQ, priority handlingbetween UEs by means of dynamic scheduling, priority handling betweenlogical channels of one UE, and logical channel prioritization.

The PDCP is specified by 3GPP. The PDCP may be located in the radioprotocol stack in the air interface on top of the RLC layer. PDCP mayprovide its services to the RRC and user plane upper layers, e.g., IP atthe UE or to the relay at the base station. PDCP may provide one or moreof the following to upper layers: transfer of user plane data; transferof control plane data; header compression; ciphering; and integrityprotection.

In some embodiments, GPS and/or BS simulation components 36 and/or 38may communicate at any suitable layer of the OSI model with the basestation networking device.

In some embodiments, components 34, 36, and/or 38 may simulatehypothetical scenarios that cannot be tested in the real world, such asby repeatedly undergoing or adjusting atmospheric conditions that affectsignal propagation and interference.

In some embodiments, BS simulation component 38 may implement one ormore virtual base stations that communicate with one or more actual basestation apparatuses 60 over a wired (or wireless) interface, such as thebackhaul connection. In some implementations, this wired interface mayuse a same protocol (e.g., Ethernet) as used by switch 55 (and evenswitch 56, in some implementations). In other implementations, thiswired interface may employ a different communications protocol. Inimplementations where the base station networking device forms part ofan eNodeB, an X2 interface may be used to interface between the basestations.

In some embodiments, base station apparatus 60 comprises switches 55and/or 56, and it may optionally comprise another switch forcommunication between the base station networking device and one or morevirtual or actual BSs. In some embodiments, communication between basestation apparatuses 60 may traverse one of the switches of the basestation networking device. In some embodiments, simulated wireless data,actual wireless data, simulated GPS data, and/or actual GPS data may notrequire a switch for communicating with the base station networkingdevice. In other embodiments, switch(es) or at least one othernetworking device may be used for this communication.

In some embodiments, BS simulation component 38 may store in electronicstorage 22 actual data of the backhaul network that comprisescommunication to and/or from a rogue base station. For example, thisbase station may be detected to be fraudulent as such, after alreadyhaving operated in this network. In some embodiments, BS simulationcomponent 38 may then inject this stored communication data into thebackhaul network to simulate ability of base station apparatus 60 toproperly identify or detect another rogue base station. As a result,network operators may better manage a backhaul network. In someembodiments, BS simulation component 38 may be automated to more rapidlyreact to such nefarious nodes. And BS simulation component 38 may employa model from database 40-2 to more quickly identify rogue base stations.In some embodiments, BS simulation component 38 may identify rogue basestations that operate via a wireless network, rather than through awired backhaul network.

In some implementations, system 10 may be a standalone, cellularcommunications system used in emergency situations (e.g., after anatural disaster) to develop and test software under hypotheticalscenarios, e.g., where environmental aspects are unstable or removed.For example, system 10 may be used to simulate or test effects ofcomponents coming back online.

FIG. 3 illustrates method 100 for preparing a simulator for a networkingsystem, in accordance with one or more embodiments. And FIGS. 4-5illustrate methods 200 and 250 for preparing a machine learner, inaccordance with one or more embodiments. Each of methods 100, 200, and250 may be performed with a computer system comprising one or morecomputer processors and/or other components. The processors areconfigured by machine readable instructions to execute computer programcomponents. The operations of methods 100, 200, and 250 presented beloware intended to be illustrative. In some embodiments, methods 100, 200,and 250 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of methods 100, 200, and250 are illustrated in FIGS. 3-5 and described below is not intended tobe limiting. In some embodiments, methods 100, 200, and 250 may each beimplemented in one or more processing devices (e.g., a digitalprocessor, an analog processor, a digital circuit designed to processinformation, an analog circuit designed to process information, a statemachine, and/or other mechanisms for electronically processinginformation). The processing devices may include one or more devicesexecuting some or all of the operations of methods 100, 200, and 250 inresponse to instructions stored electronically on an electronic storagemedium. The processing devices may include one or more devicesconfigured through hardware, firmware, and/or software to bespecifically designed for execution of one or more of the operations ofmethods 100, 200, and 250.

At operation 102 of method 100, actual wireless data of actual UEs andsimulated wireless data of simulated UEs may be accessed, via a basestation (BS) networking device of a BS apparatus. As an example, BSnetworking device of BS apparatus 60-1 may remotely obtain actualcellular data from UE 90 and locally-generated (e.g., simulated)cellular data from UE simulation component 34 (shown in FIG. 1 anddescribed herein). In another example, the BS networking device of BSapparatus 60-1 may remotely obtain actual GPS data from satellite 80 andlocally-generated (e.g., simulated) GPS data from GPS simulationcomponent 36 (shown in FIG. 1 and described herein).

At operation 104 of method 100, the access to the actual wireless dataand to the locally-generated simulated wireless data may be controlled,via an interface controller of the BS apparatus. As an example,interface control component 30 (shown in FIG. 1 and described herein)may enable (e.g., via switch 55 of FIG. 1) the access to the actualwireless data when a simulation is being prepared. In another example,interface control component 30 may disable (e.g., via switch 55 ofFIG. 1) the access to the actual wireless data and enable the access tothe simulated wireless data when a simulation is being performed. Inanother example, interface control component 30 may enable or disable(e.g., via switch 56 of FIG. 1) the access to the actual GPS data, andit may enable or disable the access to the simulated GPS data dependingon whether a simulation is being prepared or performed, respectively.

At operation 106 of method 100, a conversion may occur between wirelessand wired protocols, the actual data being accessed using a wirelessprotocol and the simulated data being accessed using a wired protocol.As an example, bridge 50 (shown in FIG. 1 and described herein) mayconvert actual cellular data to Ethernet frames. In this or anotherexample, bridge 50 may convert Ethernet frames to the cellular dataformat. In another example, bridge 51 (shown in FIG. 1 and describedherein) may convert between actual GPS data and Ethernet frames.

At operation 108 of method 100, the converted actual data may be storedfor future testing at a BS, the stored data exhibiting one or morebehaviors or patterns. As an example, UE simulation component 34 maystore the converted data in electronic storage 22, for replaying in asimulation. In another example, GPS simulation component 36 may storethe converted data in electronic storage 22, for replaying in anothersimulation.

At operation 202 of method 200, a machine learning (ML) model may becreated using simulated wireless data, the simulated wireless datahaving known values. As an example, model 40-2 may be developed bytraining component 32 (shown in FIG. 1 and described herein) usingsimulated cellular data obtained via UE simulation component 34 andelectronic storage 22. In another example, model 40-2 may be developedby training component 32 using simulated GPS data obtained via GPSsimulation component 36 and electronic storage 22.

At operation 204 of method 200, reception of actual wireless data may behalted to train the ML model by replaying a recorded scenario ofpreviously actual wireless data (e.g., training data) that exemplifies acertain behavior or pattern. As an example, interface control component30 may disable actual wireless traffic at switch 55 and/or 56. In someembodiments, training component 32 may divide or split training data40-1 (shown in FIG. 1 and described herein) into training, validation,and test datasets, in any suitable fashion. In this example, 80% of thetraining data may be used for training or validation, and the other 20%may be used for validation or testing. At least some of the trainingdata may be labeled. In some embodiments, training component 32 maytrain machine-learning prediction model 40-2 using the split trainingdata (e.g., comprising labeled training data and unlabeled test data).In some implementations, the validation data may be used to configurethe hyperparameters of the model. As an example, UE simulation component34 may obtain actual cellular data from electronic storage 22 and feedthis data in a fashion that mimics live cellular data into the BSnetworking device of BS apparatus 60-1, for performing model 40-2training. In another example, GPS simulation component 36 may obtainactual GPS data from electronic storage 22 and feed this data in afashion that mimics live GPS data into the BS networking device of BSapparatus 60-1, for performing model 40-2 training.

Models database 40-2 may comprise several different machine learningmodels. For example, one model 40-2, for training on wireless dataoriginally captured from UEs 90-91, may be different from another model40-2, for training on wireless data originally captured from satellite80. Prediction models 40-2 may each be tested to see if it hasconverged. In some implementations, model convergence implies training amodel to the extent that validation results of the model aresatisfactory. In some embodiments, convergence occurs once the model issufficiently trained. And, in other embodiments, convergence occurs oncethe model is sufficiently validated or tested. In some embodiments,training component 32 may achieve convergence for a model, whenclassification errors with respect to the validation dataset areminimized.

At operation 206 of method 200, behaviors or patterns involving a set ofactual wireless data may be learned by applying the trained ML model,when the halting is reverted to begin receiving again the actualwireless data in real-time. As an example, after interface controlcomponent 30 reenables actual wireless traffic at switch 55, UEsimulation component 34 may employ trained model 40-2 to learn newfeatures of live cellular data. In another example, after interfacecontrol component 30 reenables actual wireless traffic at switch 56, GPSsimulation component 36 may employ another, trained model 40-2 to learnnew features of live GPS data.

At operation 208 of method 200, one or more parameters of a BSnetworking device may be updated based on the newly learned behaviors orpatterns. As an example, UE simulation component 34 may leverage thesenew features to predict required capacity or coverage of the BSnetworking device in an upcoming scenario. In another example, GPSsimulation component 36 may leverage other, new features to predict anoperating parameter of the BS networking device in an upcoming scenario.

At operation 252 of method 250, an ML model using simulated backhauldata may be created, the simulated backhaul data having known values. Asan example, model 40-2 may be developed by training component 32 (shownin FIG. 1 and described herein) using simulated backhaul data obtainedvia BS simulation component 38 and electronic storage 22.

At operation 254 of method 250, reception of actual backhaul data may behalted to train the ML model by replaying a recorded scenario ofpreviously actual backhaul data (e.g., training data) that exemplifies acertain behavior or pattern. As an example, interface control component30 may disable actual backhaul traffic at a switch of BS apparatus 60.In some embodiments, training component 32 may divide or split trainingdata 40-1 (shown in FIG. 1 and described herein) into training,validation, and test datasets, in any suitable fashion. In someembodiments, training component 32 may train machine-learning predictionmodel 40-2 using the split training data (e.g., comprising labeledtraining data and unlabeled test data). As an example, BS simulationcomponent 38 may obtain actual backhaul data from electronic storage 22and feed this data in a fashion that mimics live backhaul data into theBS networking device of BS apparatus 60-1, for performing model 40-2training.

At operation 256 of method 250, behaviors or patterns involving a set ofactual backhaul data may be learned by applying the trained ML model,when the halting is reverted to begin receiving again the actualbackhaul data. As an example, after interface control component 30reenables actual backhaul traffic at the switch, BS simulation component38 may employ trained model 40-2 to learn new features of live backhauldata.

At operation 258 of method 250, an operating parameter of a BSnetworking device may be updated based on the newly learned behaviors orpatterns. As an example, BS simulation component 38 may leverage thesenew features to predict an operating parameter of the BS networkingdevice in an upcoming scenario.

At operation 260 of method 250, presence of a certain class of BSnetworking devices (e.g., a rogue BS) may be detected using the trainedML model and another set of actual backhaul data, the certain classindicating fraudulence. As an example, BS simulation component 38 mayapply this model to predict presence of a fraudulent BS networkingdevice.

Techniques described herein can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The techniques can be implemented as a computerprogram product, i.e., a computer program tangibly embodied in aninformation carrier, e.g., in a machine-readable storage device, inmachine-readable storage medium, in a computer-readable storage deviceor, in computer-readable storage medium for execution by, or to controlthe operation of, data processing apparatus, e.g., a programmableprocessor, a computer, or multiple computers. A computer program can bewritten in any form of programming language, including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer program canbe deployed to be executed on one computer or on multiple computers atone site or distributed across multiple sites and interconnected by acommunication network.

Method steps of the techniques can be performed by one or moreprogrammable processors executing a computer program to performfunctions of the techniques by operating on input data and generatingoutput. Method steps can also be performed by, and apparatus of thetechniques can be implemented as, special purpose logic circuitry, e.g.,an FPGA (field programmable gate array) or an ASIC (application-specificintegrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, such as,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as, EPROM, EEPROM, and flash memorydevices; magnetic disks, such as, internal hard disks or removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated inspecial purpose logic circuitry.

Several embodiments of the invention are specifically illustrated and/ordescribed herein. However, it will be appreciated that modifications andvariations are contemplated and within the purview of the appendedclaims.

What is claimed is:
 1. A method for providing communication for one ormore user equipment (UEs), the method comprising: accessing, via a basestation networking device, a conversion of (i) actual wireless datapertaining to actual radio frequency (RF) energy of an environmentincluding actual UEs and (ii) simulated wireless data pertaining tosimulated RF energy of an environment including simulated UEs;controlling, via an interface controller communicably coupled to thebase station networking device, the access; generating, via a processor,the simulated wireless data to perform a simulation of network trafficin a closed network; the converting, via a device, from a wirelessprotocol to a wired protocol, wherein the accesses are of protocol dataunits (PDUs) that comprise the converted data; and testing the basestation networking device for resolution of an intermittent problem byautomatically varying base station networking device parameter settings.2. The method of claim 1, wherein the actual wireless data is receivedand transmitted by one or more antennas using the wireless protocol, andwherein the converted simulated wireless data is received using thewired protocol.
 3. The method of claim 2, wherein the wireless protocolis one or more of 2G cellular, 3G cellular, 4G cellular, 4G long termevolution (LTE) cellular, 5G cellular, Wi-Fi, RF identification (RFID),Bluetooth, and Zigbee, and wherein the wired protocol is one or more ofEthernet, universal serial bus (USB), synchronous optical networking(SONET), synchronous digital hierarchy (SDH), point-to-point protocol(PPP), high-level data link control (HDLC), digital subscriber line(DSL), integrated services digital network (ISDN), fiber distributeddata interface (FDDI), and advanced data communication controlprocedures (ADCCP).
 4. The method of claim 1, wherein the conversionsare performed from the wireless protocol to the wired protocol withoutadjusting a format of the wireless data.
 5. The method of claim 1,wherein the base station networking device is further configured toprovide communication for the base station networking device and one ormore other base station networking devices of one or more other basestation networking devices, via the wired protocol.
 6. The method ofclaim 1, wherein the interface controller and the provided device areintegrated into an existing cellular system, and wherein the basestation networking device is a hub, switch, repeater, bridge, or router.7. The method of claim 1, further comprising: providing a processor thatgenerates simulated global positioning system (GPS) data such that thebase station networking device emulates being in different locations;and communicably interposing another interface controller between thebase station networking device, an actual GPS device, and the providedprocessor.
 8. The method of claim 1, wherein the interface controller isconfigured to simultaneously receive the actual and simulated wirelessdata.
 9. A method for providing communication for one or more UEs, themethod comprising: accessing, via a base station networking device, aconversion of (i) actual wireless data pertaining to actual RF energy ofan environment including actual UEs and (ii) simulated wireless datapertaining to simulated RF energy of an environment including simulatedUEs; controlling, via an interface controller communicably coupled tothe base station networking device, the access; generating, via aprocessor, the simulated wireless data to perform a simulation ofnetwork traffic in a closed network; the converting, via a device, froma wireless protocol to a wired protocol, wherein the accesses are ofPDUs that comprise the converted data; and testing the base stationnetworking device for resolution of an intermittent problem byautomatically varying a simulation of wireless interference.
 10. Themethod of claim 9, further comprising: storing the actual data, whereinthe actual data exemplifies a behavior or pattern.
 11. The method ofclaim 10, further comprising: receiving, from the simulated UEs, othersimulated data that comprises the stored data such that a response of anetworking node to the behavior or pattern is tested.
 12. The method ofclaim 11, further comprising: determining required capacity of the nodefor a future scenario different from one that exemplifies the behavioror pattern.
 13. The method of claim 9, wherein the actual UEs are remotefrom a node that performs the accessing of the actual data, and whereinthe simulated UEs are local to the node.
 14. The method of claim 9,wherein the actual and simulated data are cellular traffic.
 15. A methodfor providing communication for one or more UEs, the method comprising:accessing, via a base station networking device, a conversion of (i)actual wireless data pertaining to actual RF energy of an environmentincluding actual UEs and (ii) simulated wireless data pertaining tosimulated RF energy of an environment including simulated UEs;controlling, via an interface controller communicably coupled to thebase station networking device, the access; generating, via a processor,the simulated wireless data to perform a simulation of network trafficin a closed network; the converting, via a device, from a wirelessprotocol to a wired protocol, wherein the accesses are of PDUs thatcomprise the converted data; and testing the base station networkingdevice for resolution of an intermittent problem by automaticallyvarying a number of the simulated UEs that are generating traffic to alevel that satisfies a congestion criterion.
 16. The method of claim 15,wherein the actual wireless data is received and transmitted by one ormore antennas using the wireless protocol.
 17. The method of claim 16,wherein the converted simulated wireless data is received using thewired protocol.
 18. The method of claim 17, wherein the interfacecontroller is controlled by one or more processors.
 19. The method ofclaim 18, wherein the simulated wireless data is generated by the one ormore processors.
 20. The method of claim 15, wherein the wirelessprotocol is one or more of 2G cellular, 3G cellular, 4G cellular, 4G LTEcellular, 5G cellular, Wi-Fi, RFID, Bluetooth, or Zigbee, and whereinthe wired protocol is one or more of Ethernet, USB, SONET, SDH, PPP,HDLC, DSL, ISDN, FDDI, or ADCCP.